An automatic differentiation platform: odyssée
Future Generation Computer Systems
An automatic differentiation platform: Odyssée
Future Generation Computer Systems
Automatic differentiation for optimum design, applied to sonic boom reduction
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
Linearity analysis for automatic differentiation
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
The Tapenade automatic differentiation tool: Principles, model, and specification
ACM Transactions on Mathematical Software (TOMS)
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The reverse or adjoint mode of automatic differentiation is software engineering technique that permits efficient computation of gradients. However, this technique requires a lot of temporary memory. In this chapter, we present a refinement that reduces memory consumption in the case of parallel loops, and we give a proof of its correctness based on properties of the data-dependence graph of adjoint programs and parallel loops. This technique is particularly suitable for assembly loops that dominate in mesh-based computations. Application is done on the kernel of a realistic Navier-Stokes solver.